Frequently Asked Questions

The short answer: it depends on how much human creativity went into it.

As of 2025, the US Copyright Office’s position — confirmed in multiple rulings and policy statements — is that copyright requires human authorship. A work produced entirely by AI with no human creative contribution is not copyrightable in the US.

However, works with substantial human creative input — including AI-assisted works — can qualify for copyright protection for the human-authored portions. The Copyright Office has registered works that combine AI-generated elements with meaningful human creative choices, including a graphic novel that used AI-generated images arranged and captioned by a human author.

What this means in practice for music

Scenario	Copyright status
You pressed “generate” on Suno and uploaded the result unchanged	Not copyrightable (no human authorship)
You wrote lyrics, arranged the structure, and used AI to generate the backing track	Your lyrics and arrangement choices are protectable
You used AI stems as raw material and substantially reworked them	Protected based on your creative contributions
You composed the melody by hand and used AI for mastering only	Fully protected — mastering is a technical process, not authorship

Outside the US

Other jurisdictions have different rules. The UK copyright act does not require human authorship and has a specific provision for “computer-generated works” (Section 9(3) CDPA 1988), though its scope is actively debated. EU member states generally require human creative expression.

The evolving landscape

Multiple court cases and legislative proposals are working through the system simultaneously. The landscape will look different in 2–3 years. The safest approach: document your human creative contributions carefully regardless of the current rules, so you’re well-positioned when the law clarifies.

Royalties for AI-generated music are genuinely complicated — and the system is still catching up to the technology. Here’s a practical breakdown of what you need to know today.

The two main royalty streams

1. Master recording royalties (sound recording copyright) These come from streaming plays, downloads, sync licenses, and broadcast. They flow to whoever owns the master recording.

For AI-generated music: if you own the generated output (check your tool’s ToS), you can register it and collect performance royalties via SoundExchange and neighbouring rights organisations. Whether AI output qualifies for copyright protection is jurisdiction-dependent (see our copyright FAQ), but collecting organisations will register and pay out regardless — they’re not the copyright adjudicators.

2. Publishing royalties (musical work copyright) These come from mechanical royalties (streams, downloads), performance royalties (radio, venues), and sync fees. They flow to the songwriter and publisher.

For AI-generated music: if the composition is your original work (lyrics you wrote, melody you composed), your PRO (ASCAP, BMI, SESAC in the US; PRS, SACEM, GEMA in the UK/EU) will collect performance royalties from radio, TV, and streaming. Register every composition.

Platform-specific rules

Platform	AI music policy
Spotify	Requires disclosure of AI-generated content; no blanket ban
Apple Music	Requires distributor disclosure; reserves right to remove undisclosed AI content
YouTube	AI-generated music allowed; monetization available; Content ID applies
DistroKid	Added AI disclosure checkbox in 2024; routes eligible content to platforms
CD Baby	Requires AI disclosure; screens for voice cloning violations

What to do before distributing

1. Check your tool’s ToS: Suno, Udio, and AIVA have different commercial licensing terms — confirm you own or have licensed the output
2. Register the composition with your PRO before distribution
3. Get an ISRC for each recording via your distributor or SoundExchange
4. Disclose AI involvement to your distributor — most now require it
5. Consult a music attorney for releases you expect to generate significant income — the rules are genuinely unsettled and the stakes rise with the numbers

Both Suno and Udio are text-to-music AI platforms that generate complete songs — including vocals and instrumentation — from a text prompt. They’re often compared because they occupy the same category, but they have meaningfully different strengths.

Suno

Best for: Quick song generation, demo sketches, hooks, pop/mainstream genres

• Generates complete 2–4 minute songs in seconds
• Strong vocal performance (melody, phrasing, expression)
• Clean, polished output by default — sounds “radio-ready”
• Custom Mode lets you specify lyrics or instrumental sections
• Stem download available on Pro tier
• Suno v4 introduced significantly more stylistic range and longer compositions

Typical use case: You have a lyrical concept and want to hear how it sounds with a full arrangement in 30 seconds.

Udio

Best for: Genre fidelity, unusual styles, atmospheric and textural music

• Better at replicating the sonic signature of specific genres (blues, jazz, metal sub-genres, regional music styles)
• More texture and “grain” — sounds less processed/polished, which is sometimes exactly right
• More flexible with unusual style descriptors
• Remix and extension features let you evolve a clip rather than regenerating from scratch
• Output can require more editing to reach distribution quality

Typical use case: You want something that genuinely sounds like 1970s Ethiopian jazz or early 2000s shoegaze, not a pop approximation of it.

Which should you use?

Situation	Recommendation
You want something fast, polished, and mainstream	Suno
You’re demoing a pop/R&B/hip-hop concept	Suno
You need genre accuracy over polish	Udio
You’re working in a niche or historical style	Udio
You want to extend or remix a clip iteratively	Udio
You want to download stems	Suno (Pro tier)

Most serious practitioners use both. The workflows complement each other — generate options in Suno for speed, then explore in Udio for depth.

The legal caveat

Both companies are currently subject to copyright litigation from major labels over training data. Monitor developments before commercial releases. See our FAQ on AI music copyright.

Partially, in some contexts — and the honest answer requires separating several different questions.

What AI can do today

AI can generate convincing audio for:

• Standard chord accompaniments (piano, guitar, bass lines in common styles)
• Beat production (programmed drums, electronic percussion)
• String pads and orchestral textures for film/TV/games (often adequate at budget tier)
• Reference tracks and demos (proving a musical concept before booking real players)

For these use cases — especially demos, reference tracks, and budget-constrained projects — AI tools are already displacing session bookings.

What AI cannot currently do

• Interpret the room: a session musician reads the producer’s mood, suggests an unexpected part, adapts in real time
• Push back creatively: the best session players make the song better in ways the producer didn’t anticipate
• Carry cultural authenticity: a flamenco guitarist or a Nashville fiddle player brings something rooted in a tradition that AI approximates but cannot embody
• Perform with physical expression: the microtiming, dynamics, and imperfections of human performance are the difference between music that feels alive and music that feels generated
• Collaborate in a relationship: session musicians become trusted collaborators over years; that relationship has real commercial and creative value

What’s actually happening in the industry

The clearest displacement is at the lower end of the session market: jingle work, stock music, basic sync placements, and demo tracks. These categories were already price-sensitive, and AI has made them near-free.

Mid-tier and high-end session work — flagship productions, film scores, live touring — has been less affected. Producers at that level use AI for exploration and scaffolding, then hire session players for the final recording.

The concern is less about complete replacement and more about compression of the middle: the working session musician economy that sustained thousands of professionals is under significant pressure even if the top tier remains human.

What session musicians can do

The most resilient session musicians today are those who:

• Offer something specifically human: personality, performance authority, interpretive judgment
• Collaborate early in the process, not just as executors of finished arrangements
• Develop skills in AI tool integration — becoming the bridge between AI-generated sketches and human-quality performances
• Build personal brand and direct relationships with artists who value authentic performance

Stem separation (also called source separation or unmixing) is the process of isolating individual audio components — vocals, drums, bass, guitars, keyboards — from a mixed recording. What used to require the original multitrack session can now be done in seconds using AI.

How it works

Traditional mixing takes individual tracks (stems) and combines them into one stereo mix. Stem separation runs that process in reverse, using machine learning models trained on thousands of songs to predict where each instrument’s audio is “hidden” within the combined mix.

The models use spectral analysis — examining the frequency, timing, and timbral fingerprint of each source — combined with phase cancellation techniques to pull individual elements apart. Modern deep learning models (U-Net, Demucs, HTDemucs) can separate 4–6 stems with surprisingly low bleed in most genres.

Common separation tools

Tool	Stems	Notes
LALAL.AI	10 types	Web and desktop; fast; commercial use allowed
Demucs (Meta)	4–6	Open-source; runs locally; excellent quality
Spleeter (Deezer)	2–5	Open-source; older but still widely used
iZotope RX	Dialogue, music, noise	Professional broadcast tool; expensive
Moises	4–5	Mobile-friendly; good for quick work

What you can do with separated stems

• Remixing: replace one element (e.g., drums) with your own production
• Transcription: isolate the bass line or melody to transcribe by ear accurately
• Karaoke / practice tracks: remove vocals for instrumental backing tracks
• Sample extraction: isolate a specific instrument for use as a sample (check copyright)
• Analysis: study the arrangement of a reference track by listening to each element individually
• Vocal repair: extract a vocal from a recording to clean up, pitch-correct, or reimagine

Limitations

• Bleed: energy from other instruments leaks into separated stems, especially in dense mixes
• Genre sensitivity: works best on music with clear separation between instruments; struggles with noise music, live orchestral recordings, heavily layered electronic music
• Copyright: separating a copyrighted commercial recording does not grant you rights to use those stems — legal use depends on jurisdiction and intended purpose